P028

Synchronous Feature-Tuning for Underwater Image Segmentation

A0072 Wen-Shiuan Shie Department of Electrical Engineering, National Taiwan Ocean University

A0073 Jung-Hua Wang Department of Electrical Engineering, National Taiwan Ocean University

Image segmentation plays an important role for underwater object

recognition. A new approach, called WA-SFT, which incorporates the

Watershed Analysis and a Synchronous Feature-Tuning (SFT) algorithm to

perform fast underwater image segmentation, is presented. Currently,

most watershed-based segmentation methods, merge regions one by one to

alleviate the over-segmentation problem. However, sequential merging

would inevitably incur lengthy computation time. The SFT algorithm

simultaneously tunes features of regions by referring to adjacent

regions. Due to the use of synchronous strategy, SFT achieves fast

merging and provides great potentiality for a fully parallel hardware

implementation. The iterative operation of WA-SFT converges when the

numbers of merged regions in two successive iterations are identical.

Empirical results show that WA-SFT outperforms other methods in terms

of computation efficiency and segmentation accuracy.